class_docstr: Apply multiplicative 1-centered Gaussian noise.
  
  As it is a regularization layer, it is only active at training time.
  
  Args:
      rate: Float, drop probability (as with `Dropout`).
          The multiplicative noise will have
          standard deviation `sqrt(rate / (1 - rate))`.
      seed: Integer, optional random seed to enable deterministic behavior.
  
  Call arguments:
      inputs: Input tensor (of any rank).
      training: Python boolean indicating whether the layer should behave in
          training mode (adding dropout) or in inference mode (doing nothing).
code: def get_config(self):
      base_config = super().get_config()
      config = {
          "rate": self.rate,
          "seed": self.seed,
      }
      return {**base_config, **config}
  
file_path: keras/keras/layers/regularization/gaussian_dropout.py
imports: import math
  from keras import backend
  from keras import layers
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 4 for calling the GaussianDropout obj's underlying member method code with example usage: obj.get_config() and returns: dict